ATLAS Data Management Status (PowerPoint)

The History and Future of ATLAS Data
Management Architecture
D. Malon, S. Eckmann, A. Vaniachine (ANL),
J. Hrivnac, A. Schaffer (LAL), D. Adams (BNL)
CHEP’03
San Diego, California
24 March 2003
Outline
 Persistent principles
 Pre-POOL: The ATLAS event store architecture
 Hybrid event stores
 ATLAS and POOL
 Non-event data in ATLAS
 ATLAS data management and grids
 ATLAS data management and other emerging technologies
David M. Malon, ANL CHEP'03, San Diego 24 March 2003 2
Long-standing principles
 Transient/persistent separation—not by any means unique to ATLAS—
means (in ATLAS, anyway):
 Physics code does not depend on storage technology used for input or
output data
 Physics code does not “know” about persistent data model
 Selection of storage technology, explicit and implicit, involves only job
options specified at run time
 Commitment to use common LHC-wide solutions wherever possible,
since at least the time of the ATLAS Computing Technical Proposal
 Once this implied Objectivity/DB (RD45)
 Now this implies LCG POOL
David M. Malon, ANL CHEP'03, San Diego 24 March 2003 3
Pre-POOL: The ATLAS event store
architecture
 Event collection is fundamental: processing model is “read one or more
collections, write one or more collections”
 Note: collections are persistent realizations of something more general
 Model allows navigational access, in principle, to all upstream data
 Output “event headers” retain sufficient information to reach any data
reachable from input event headers
 Architecture supports strategies for “sharing” data, so that writing
events to multiple streams, for example, does not require (but may
allow) replication of component event data
David M. Malon, ANL CHEP'03, San Diego 24 March 2003 4
Pre-POOL: The ATLAS event store
architecture - II
 Data selection model is, in relational terms, “SELECT … FROM …
WHERE …”
 SELECT which components of event data?
 FROM which event collection(s)?
 WHERE qualifying events satisfy specified conditions
 Designed to allow server-side and client-side selection implementation
 Architecture also describes a placement service, though our
implementations were rather rudimentary
 Mechanism for control of physical clustering of events (e.g., by stream), of
event components (e.g., by “type”), and for handling file/database allocation
and management
 Interface could satisfied by a within-job service, or by a common service
shared by many jobs (e.g., in a reconstruction farm)
 “Extract and transform” paradigm for selection/distribution
David M. Malon, ANL CHEP'03, San Diego 24 March 2003 5
Hybrid event stores
 There were (and are) many questions about where genuine database
functionality may be required, or useful, in an event store
 STAR, for example, had already successfully demonstrated a “hybrid”
approach to event stores:
 File-based streaming layer for event data
 Relational database to manage the files
 A hybrid prototype (AthenaROOT) was deployed in ATLAS in parallel
with the ATLAS baseline (Objectivity-based)
 Transient/persistent separation strategy supports peaceful coexistence;
physicists’ codes remain unchanged
…all of this was input to the LCG requirements technical assessment
group that led to initiation of POOL…
David M. Malon, ANL CHEP'03, San Diego 24 March 2003 6
ATLAS and POOL
 ATLAS is fully committed to using LCG persistence software as its
baseline, and to contributing to its direction and its development
 This means POOL: see earlier talks for POOL details
 What POOL provides is closer to a framework than to an architecture
 …though architectural assumptions, both implicit and explicit, go into
decisions about POOL components and their designs
 The influence is bidirectional
 Because POOL is still in its infancy, we do not fully understand its
implications for ATLAS data management architecture
 Not a criticism: even if POOL delivered all the functionality of
Objectivity/DB (or of Oracle9i), LHC experiments would still need to
define their data management architectures
David M. Malon, ANL CHEP'03, San Diego 24 March 2003 7
Non-event data in ATLAS
 While event and conditions stores may be logically distinct and
separately managed at some levels, there is no reason they should not:
 employ common storage technologies (POOL/ROOT, for example)
 register their files in common catalogs
 ATLAS currently has a variety of non-event data in relational databases
(e.g., the “primary numbers” that parameterize ATLAS detector
geometry)
 Today this entails ATLAS-specific approaches, but in principle, a POOL
MySQL(ODBC?) Storage Manager implementation could be used, as could
the LCG SEAL dictionary for data definition
 For interfaces unique to time-varying data, e.g., access based on
timestamps and intervals of validity, ATLAS again hopes to employ
common LHC-wide solutions where possible
David M. Malon, ANL CHEP'03, San Diego 24 March 2003 8
Architectural principles for time-varying
data
 Separate the interval-of-validity (IOV) database infrastructure from
conditions data storage.
 Should be possible to generate and store conditions data in any
supported technology (POOL ROOT, MySQL, plain ASCII files, …)
without worrying about the interval-of-validity infrastructure
 Generation of data, and assignment of intervals of validity, versions, and
tags, may be widely separated in time, and done by different people
 Later register the data in the IOV database, assigning an interval of
validity, tag, version, …
 Transient IOV service will consult IOV database to get pointer to correct
version of data, then invoke standard Athena conversion services to put
conditions objects in the transient store
David M. Malon, ANL CHEP'03, San Diego 24 March 2003 9
Transient Conditions Store
4. Build transient conditions object
Conditions data
2. Ref to data
1. Timestamp, tag, version
IOV Database
David M. Malon, ANL CHEP'03, San Diego 24 March 2003 10
Nonstandard data?
 Cannot expect that all useful non-event data are produced by ATLAS-
standard tools
 How do such data enter ATLAS institutional memory?
 Is it as simple as registration in an appropriate file catalog (for files, anyway)
managed by the ATLAS virtual organization?
 Is there a minimal interface such data must satisfy?
 ATLAS “dataset” notions are relevant here
 Ability to externalize a pointer to an object in this technology?
 What is required in order for an application in the ATLAS control
framework (Athena) to access such data?
 Provision of an LHCb/Gaudi-style conversion service?
 LCG SEAL project may influence our answers to these questions
David M. Malon, ANL CHEP'03, San Diego 24 March 2003 11
ATLAS data management and grids
 With the current state of grid tools, grid data management has meant,
primarily, file replica cataloging and transfer, with a few higher-level
services
 ATLAS has prototyped and used a range of grid replica catalogs
(GDMP, EDG, Globus (pre-RLS), RLS,…), grid file transport tools, grid
credentials
 Principal tool for production purposes is MAGDA (ATLAS-developed)
 MAGDA designed so that its components can be replaced by grid-standard
ones as they become sufficiently functional and mature
 This has already happened with transport machinery; will happen with replica
catalog component as RLS implementations improve
David M. Malon, ANL CHEP'03, San Diego 24 March 2003 12
Databases on grids
 Need more detailed thinking on how to deal with database-resident data
on grids
 Do Resource Brokers know about these?
 Connections between grid replica tracking and management and database-
provided replication/synchronization, especially when databases are
updateable
 Have looked a bit at EDG Spitfire
 Could, in some cases, transfer underlying database files via replication
tools, and register them (a la GDMP with Objectivity/DB databases)
 Have done some prototyping with MySQL embedded servers
 On-demand access over the net poses some challenges
 Grid/web service interfaces (OGSA) should help
David M. Malon, ANL CHEP'03, San Diego 24 March 2003 13
Recipe management, provenance, and
“virtual data”
 Every experiment maintains, via software repositories and managed
releases, official versions of recipes used to produce data
 Everyone logs the recipes (job options, scripts) used for official
collaboration data production in a bookkeeping database
 ATLAS does this in its AMI bookkeeping/metadata database
 Virtual data prototyping has been done in several ATLAS contexts
 Parameterized recipe templates (transformations), with actual parameters
supplied and managed by a database in DC0/1 (derivations)
 See Nevski/Vaniachine poster
 Similar approach in AtCom (DC1)
 GriPhyN project’s Chimera virtual data catalog and Pegasus planner, used
in SUSY data challenge, and (currently) for reconstruction stage of Data
Challenge 1
David M. Malon, ANL CHEP'03, San Diego 24 March 2003 14
Provenance
 “Easy” part of provenance is at the “job-as-transformation” level:
 What job created this file?
 What job(s) created the files that were input to that job?
 …and so on…
 But provenance can be almost fractal in its complexity:
 An event collection has a provenance, but provenance of individual events
therein may be widely varying
 Within each of those events, provenance of event components varies
 Calibration data used to produce event component data have a provenance
 Values passed as parameters to algorithms have a provenance
 …
David M. Malon, ANL CHEP'03, San Diego 24 March 2003 15
Some provenance challenges
 CHALLENGES:
 Genuinely browsable, queryable transformation/derivation catalogs,
with sensible notions of similarity and equivalence
 Integration of object-level history tracking, algorithm version
stamping, …., (currently experiment-specific), with emerging
provenance management tools
David M. Malon, ANL CHEP'03, San Diego 24 March 2003 16
The metadata muddle
 Ill-defined: One man’s metadata is another man’s data
 Essential, though: a multi-petabyte event store will not be navigable
without a reasonable metadata infrastructure
 Physicist should query a physics metadata database to discover what
data are available and select data of interest
 Metadata infrastructure should map physics selections to, e.g., lists of
logical files, so that resource brokers can determine where to run the
job, what data need to be transferred, and so on
 Logical files have associated metadata as well
 Some metadata about provenance is principally bookkeeping, but some
is as useful as physics properties to physicists trying to select data of
interest
David M. Malon, ANL CHEP'03, San Diego 24 March 2003 17
Metadata integration?
 Current ATLAS data challenge work distinguishes 1.) physics metadata, 2.)
metadata about logical and physical files, 3.) recipe/provenance metadata, 4.)
permanent production bookkeeping, and 5.) transient production log data, as a
starting point
 It is not too hard to build an integrated system when the components are all
under the experiment’s control, but when replica metadata management is
coming from one project, provenance metadata management from another,
physics, perhaps, from the experiment itself, bookkeeping from (perhaps) still
another source, a system that supports queries across layers is a CHALLENGE
 …in ATLAS, we still do not quite have a system that lets a physicist choose a
physics sample as input, and emits EDG JDL, for example, and this is just a
small step
David M. Malon, ANL CHEP'03, San Diego 24 March 2003 18
Beyond persistence
 Persistence—saving and restoring object states—is a minimalist view:
it is necessary, but is it sufficient?
 Should the object model (in transient memory) of the writer determine
the view that clients can extract from an event repository?
 “Schema evolution” does not suffice:
 Schema evolution recognizes that, though I write objects {A, B, C, D} today,
the class definitions of A, B, C, and D may change tomorrow
 It fails to recognize that my object model may use entirely different classes
{E,F, G} in place of {A, B, C, D} next year
 Simple persistence fails to acknowledge that readers may not want the
same objects that writers used, and that not all readers share a single
view
David M. Malon, ANL CHEP'03, San Diego 24 March 2003 19
Beyond persistence: a trivial example
 Can a reader build a “simple” track (AOD track) from a “full track” data
object (the saved state of an object of a different class), without creating
an intermediate “full track” on the client side?
 In a relational database, I can write a 500-column table but read and
transfer to clients the data from only three of the columns
 Simplified view: Need an infrastructure that can answer the question,
“Can I build an object of type B from the data pointed to by this
persistent reference (perhaps the saved state of an object of type A)?”
David M. Malon, ANL CHEP'03, San Diego 24 March 2003 20
LCG Object Dictionary: Usage (diagram
thanks to Pere Mato)
.adl
.h
.xml
Population
ROOTCINT GCC-XML ADL/GOD
CINT generated
Dict generating
code
code
Conversion (1) in
Dict gateway
LCG to CINT
LCG
CINT Dictionary Other
Streamer
Dict Clients:
(python,
ROOT I/O (2) out GUI, etc.)
Reflection
Requires elaboration
David M. Malon, ANL CHEP'03, San Diego 24 March 2003 21
Elaboration?
 Selection of persistent representation and streamer generation can be
separated
 More than one persistent representation may be supported
 Custom streamers
 Separation of transient object dictionaries and persistent layout dictionaries
 On input, what one reads need not dictate what one builds in transient memory
 Not “Ah! This is the state of a B; I’ll create a transient B!”
 Rather, “Can I locate (or possibly create) a recipe to build a B from these
data?”
Conversion
Dict gateway
LCG to CINT
CINT
Streamer
Dict
ROOT I/O
David M. Malon, ANL CHEP'03, San Diego 24 March 2003 22
Other emerging ideas
 Current U.S. ITR proposal is promoting knowledge management in
support of dynamic workspaces
 One interesting aspect of this proposal is in the area of ontologies
 An old term in philosophy (cf. Kant), a well-known concept in the (textual)
information retrieval literature, and a hot topic for semantic web folks
 Can be useful when different groups define their own metadata, using
similar terms with similar meanings, but not identical terms with identical
meanings
 Could also be useful in defining what is meant, for example, by “Calorimeter
data,” without simply enumerating the qualifying classes
David M. Malon, ANL CHEP'03, San Diego 24 March 2003 23